Practicing Evidence-Based Medicine

Clinical Decision ◽

Medical Decision ◽

Number Needed To Treat ◽

Evidence Based ◽

Symptomatic Carotid ◽

The North ◽

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

Practicing Evidence-Based Medicine (SAGHE)

10.2310/gastro.1223 ◽

2015 ◽

Author(s):

Michael Barnett ◽

Niteesh Choudhry

Keyword(s):

Evidence Based Medicine ◽

Test Performance ◽

Clinical Decision ◽

Medical Decision ◽

Number Needed To Treat ◽

Evidence Based ◽

Symptomatic Carotid ◽

The North ◽

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

Practicing Evidence-Based Medicine

10.2310/neuro.1223 ◽

2015 ◽

Author(s):

Michael Barnett ◽

Niteesh Choudhry

Keyword(s):

Evidence Based Medicine ◽

Test Performance ◽

Clinical Decision ◽

Medical Decision ◽

Number Needed To Treat ◽

Evidence Based ◽

Symptomatic Carotid ◽

The North ◽

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

Practicing Evidence-Based Medicine

10.2310/tywc.1223 ◽

2019 ◽

Author(s):

Michael Barnett ◽

Niteesh Choudhry

Keyword(s):

Evidence Based Medicine ◽

Test Performance ◽

Clinical Decision ◽

Medical Decision ◽

Number Needed To Treat ◽

Evidence Based ◽

Symptomatic Carotid ◽

The North ◽

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

The ‘Number Needed to Treat’ and the ‘Adjusted NNT’ in Health Care Decision-Making

Journal of Health Services Research & Policy ◽

10.1177/135581969800300110 ◽

1998 ◽

Vol 3 (1) ◽

pp. 44-49 ◽

Cited By ~ 9

Author(s):

Jack Dowie

Keyword(s):

Health Care ◽

Decision Making ◽

Evidence Based Medicine ◽

Clinical Decision Making ◽

Treatment Options ◽

Clinical Decision ◽

Population Based ◽

Number Needed To Treat ◽

Evidence Based ◽

Within ‘evidence-based medicine and health care’ the ‘number needed to treat’ (NNT) has been promoted as the most clinically useful measure of the effectiveness of interventions as established by research. Is the NNT, in either its simple or adjusted form, ‘easily understood’, ‘intuitively meaningful’, ‘clinically useful’ and likely to bring about the substantial improvements in patient care and public health envisaged by those who recommend its use? The key evidence against the NNT is the consistent format effect revealed in studies that present respondents with mathematically-equivalent statements regarding trial results. Problems of understanding aside, trying to overcome the limitations of the simple (major adverse event) NNT by adding an equivalent measure for harm (‘number needed to harm’ NNH) means the NNT loses its key claim to be a single yardstick. Integration of the NNT and NNH, and attempts to take into account the wider consequences of treatment options, can be attempted by either a ‘clinical judgement’ or an analytical route. The former means abandoning the explicit and rigorous transparency urged in evidence-based medicine. The attempt to produce an ‘adjusted’ NNT by an analytical approach has succeeded, but the procedure involves carrying out a prior decision analysis. The calculation of an adjusted NNT from that analysis is a redundant extra step, the only action necessary being comparison of the results for each option and determination of the optimal one. The adjusted NNT has no role in clinical decision-making, defined as requiring patient utilities, because the latter are measurable only on an interval scale and cannot be transformed into a ratio measure (which the adjusted NNT is implied to be). In any case, the NNT always represents the intrusion of population-based reasoning into clinical decision-making.

Evidence-based medicine

10.1093/med/9780198735823.003.0007 ◽

2017 ◽

Author(s):

Philip Wiffen ◽

Marc Mitchell ◽

Melanie Snelling ◽

Nicola Stoner

Keyword(s):

Evidence Based Medicine ◽

Binary Data ◽

Number Needed To Treat ◽

Evidence Based ◽

High Quality ◽

Relative Risks ◽

High Quality Evidence ◽

Based Medicine ◽

Quality Evidence

This chapter provides a brief overview to the concept of evidence-based medicine (EBM) starting with a well-accepted definition. The importance of clinical significance over statistical significance is discussed. A number of useful tools are presented and described to enable the practitioner to become competent in recognizing high-quality evidence and to have the skills to critically appraise evidence that is potentially important to their practice. There is a brief description of some of the statistical tools commonly used in EBM including binary data tools such as odds ratios, number needed to treat, and relative risks.

Practising evidence-based medicine in an era of high placebo response: number needed to treat reconsidered

The British Journal of Psychiatry ◽

10.1192/bjp.bp.115.163261 ◽

2016 ◽

Vol 208 (5) ◽

pp. 416-420 ◽

Cited By ~ 8

Author(s):

Steven P. Roose ◽

Bret R. Rutherford ◽

Melanie M. Wall ◽

Michael E. Thase

Keyword(s):

Evidence Based Medicine ◽

Clinical Management ◽

Clinical Decision Making ◽

Placebo Response ◽

Clinical Decision ◽

Placebo Control ◽

Number Needed To Treat ◽

Therapeutic Effects ◽

Evidence Based ◽

SummaryThe number needed to treat (NNT) statistic was developed to facilitate the practice of evidence-based medicine. Placebo was assumed to be therapeutically inert when the NNT was originally conceived, but more recent data for conditions such as major depressive disorder (MDD) suggest that the placebo control condition can have considerable therapeutic effects. Complications arise because the NNT calculated from randomised controlled trials (RCTs) reflects a comparison between medication plus clinical management and placebo plus clinical management, whereas, in the clinical setting, physicians choose between prescribing open medication, observing a patient over time with a supportive approach, and doing nothing. Thus, NNTs derived from clinical trials are not directly relevant to clinical decision-making, because they are based on control conditions that do not exist in standard practice. Additional difficulties may arise when using NNTs to compare alternative treatments for MDD, such as medication and psychotherapy, since these comparisons require the control conditions upon which the respective NNTs are based to be similar. Whereas pill placebo conditions include intensive clinical management and elicit expectations of improvement, attention control conditions for psychotherapy research are less well developed. Often the effects of psychotherapy are gauged against a wait-list control condition, which has substantially fewer therapeutic components than a pill placebo control condition. To improve the clinical utility of NNTs for the treatment of MDD, we advocate effectiveness studies that include treatment conditions resembling actual clinical practice, rather than using placebo-controlled RCTs for this purpose. Until such studies are performed, the effect of bias in comparing NNTs across treatments can be controlled by ensuring that the RCT control conditions upon which the NNTs are based are comparable.

Supporting general adult psychiatry higher trainees to develop research competencies: a training improvement project

BJPsych Open ◽

10.1192/bjo.2021.377 ◽

2021 ◽

Vol 7 (S1) ◽

pp. S131-S132

Author(s):

Annalie Clark ◽

John Stevens ◽

Sarah Abd El Sayed

Keyword(s):

Evidence Based Medicine ◽

Baseline Survey ◽

Evidence Based ◽

North West ◽

The North ◽

Research Competencies ◽

Adult Psychiatry ◽

Based Medicine ◽

Training Opportunities ◽

Research Studies

AimsEvidence shows that research-active trusts have better clinical patient outcomes. Psychiatric trainees are required to develop knowledge and skills in research techniques and critical appraisal to enable them to practice evidence-based medicine and be research-active clinicians. This project aimed to evaluate and improve the support for developing research competencies available to general adult psychiatry higher trainees (HT) in the North-West of England.MethodGeneral Adult HT in the North–West of England completed a baseline survey in November 2019 to ascertain trainee's experience of research training provision. The following interventions were implemented to address this feedback:A trainee research handbook was produced, containing exemplar activies for developing research competencies and available training opportunities, supervisors and active research studies.The trainee research representative circulated research and training opportunities between November 2019 – August 2020.Research representatives held a trainee Question and Answer session in September 2020.All General Adult HT were asked to complete an electronic survey in November 2020 to evaluate the effect of these interventions.Result18 General Adult HT completed the baseline survey in November 2019. 29.4% of trainees thought they received enough information on research competencies and 88.9% wanted more written guidance. 38.9% of trainees knew who to contact about research within their NHS Trust and 33.3% were aware of current research studies. Identified challenges for meeting research competencies included lack of time, difficulty identifying a mentor and topic and accessibility of projects.20 General Adult HT completed the repeat survey in November 2020. 50% of trainees wanted to be actively involved in research and 35% wanted to develop evidence-based medicine skills. A minority of trainees aimed to complete only the minimum ARCP requirements. All trainees thought the handbook was a useful resource for meeting research competencies and would recommend it to other trainees. In trainees who received the handbook, 94.7% thought they had received adequate support on meeting research competencies and 94.7% knew who to contact about research in their trust. 68.4% of trainees would like further written guidance on meeting research competencies. Trainees highlighted ongoing practical difficulties with engaging with research and concern about lacking required skills for research.ConclusionTrainees are motivated to engage with research on various different levels, not purely for ARCP purposes. Simple interventions can help trainees feel adequately supported with meeting research competencies. Further work to support trainee involvement in research and improve trainee confidence in engaging with research is required.

Evidence-Based Medicine and Clinical Decision Making

Integrative Medicine ◽

10.1016/b978-1-4160-2954-0.50011-9 ◽

2007 ◽

pp. 47-56

Author(s):

Opher Caspi

Keyword(s):

Decision Making ◽

Evidence Based Medicine ◽

Clinical Decision Making ◽

Clinical Decision ◽

Evidence Based ◽

Impact of deliberate practice on evidence-based medicine attitudes and behaviours of health care professionals

Perspectives on Medical Education ◽

10.1007/s40037-020-00634-9 ◽

2020 ◽

Author(s):

Eelco Draaisma ◽

Lauren A. Maggio ◽

Jolita Bekhof ◽

A. Debbie C. Jaarsma ◽

Paul L. P. Brand

Keyword(s):

Health Care ◽

Decision Making ◽

Clinical Research ◽

Health Care Professionals ◽

Evidence Based Medicine ◽

Deliberate Practice ◽

Medical Decision ◽

Evidence Based ◽

Teaching Activities ◽

Abstract Introduction Although evidence-based medicine (EBM) teaching activities may improve short-term EBM knowledge and skills, they have little long-term impact on learners’ EBM attitudes and behaviour. This study examined the effects of learning EBM through stand-alone workshops or various forms of deliberate EBM practice. Methods We assessed EBM attitudes and behaviour with the evidence based practice inventory questionnaire, in paediatric health care professionals who had only participated in a stand-alone EBM workshop (controls), participants with a completed PhD in clinical research (PhDs), those who had completed part of their paediatric residency at a department (Isala Hospital) which systematically implemented EBM in its clinical and teaching activities (former Isala residents), and a reference group of paediatric professionals currently employed at Isala’s paediatric department (current Isala participants). Results Compared to controls (n = 16), current Isala participants (n = 13) reported more positive EBM attitudes (p < 0.01), gave more priority to using EBM in decision making (p = 0.001) and reported more EBM behaviour (p = 0.007). PhDs (n = 20) gave more priority to using EBM in medical decision making (p < 0.001) and reported more EBM behaviour than controls (p = 0.016). Discussion Health care professionals exposed to deliberate practice of EBM, either in the daily routines of their department or by completing a PhD in clinical research, view EBM as more useful and are more likely to use it in decision making than their peers who only followed a standard EBM workshop. These findings support the use of deliberate practice as the basis for postgraduate EBM educational activities.